Polymers are highly dynamic molecules and many of their functionally important characteristics, such as affinity for substrates and stability of active form, depend on an ensemble of the structures comprising multiple conformational sub-states, their probabilities and transition rates, and characteristics of the intrinsic free energy surface.
In the case of proteins, there is growing evidence indicating that binding of a protein to particular targets is often associated with a preferential selection of one or more of these conformations. See Smock and Gierasch, 2009, “Sending signals dynamically,” Science 324 (5924): 198-203.
Detailed structural information obtained using experimental procedures such as X-ray crystallography does not provide much information on a polymer's dynamic behavior. An understanding of polymer dynamics is especially important in studies of environmental and solvent effects on stability and macromolecular association, processes that are often accompanied by structural reorganization.
In order to develop a broader understanding of how polymers function, and to develop better industrial and pharmaceutical polymers, it is necessary to develop an appreciation for these diverse conformational states. Traditionally, conformational sampling of polymer structures is carried out using deterministic or stochastic simulation approaches. See Adcock McCammon, 2006, “Molecular dynamics: survey of methods for simulating the activity of proteins,” Chem Rev 106 (5):1589-615.
In a deterministic approach, such as molecular dynamics simulation, Newtonian mechanics is employed to calculate the trajectory of all the particles in the system as a result of the interaction forces acting between them. In this procedure, the atomic displacements are estimated at very short time-steps, and numerical integration is carried out in an iterative computation to predict the detailed system dynamics over longer time-scales. There is an upper limit to the time-step that can be used, and this, together with the large size of the polymer system being studied, computationally limits the length of the simulation that can be performed. This in turn limits the number of large-scale conformational transitions and hence the number of sub-states of the polymer that can be revealed in the course of a single trajectory. Although these traditional computational simulations address the dynamic character of polymers, molecular dynamic approaches (deterministic) are computationally intense, making them impractical for studying slow conformational changes of larger polymers, such as large proteins.
In a stochastic approach, such as Monte Carlo sampling, a number of variables in the system are randomly selected and perturbed to generate a new configuration of the system. In an evolved version of the algorithm, such as the Metropolis Monte Carlo method, the new configuration is accepted or rejected on the basis of an energetic criterion at the temperature of interest, leading to a Boltzmann weighted ensemble of thermodynamically relevant configurations. The use of Monte Carlo sampling can result in significantly more efficient jumps between relevant conformational states, thus overcoming the barriers observed in traditional molecular dynamics simulations. But selecting the degrees of freedom that, when perturbed, would result in a workable acceptance probability under the Metropolis scheme is often a major problem in the application of this approach. In other words, simulations based on purely stochastic algorithms such as the Metropolis Monte-Carlo technique have not been successful in solving this problem because they tend to yield limited acceptance ratios resulting in inefficient simulation. Methods which include stochastic moves with deterministic MD evolution have been discussed but have been limited in their application. See, Guarnieri and Still, 1994, “A rapidly convergent simulation method: Mixed Monte Carlo/stochastic dynamics,” J Comput Chem 15 (11):1302-1310; and U.S. Pat. No. 5,740,072.
Given the above background, improved systems and method of arriving at polymer conformational information is therefore needed.